000229954 001__ 229954
000229954 005__ 20181203024752.0
000229954 0247_ $$2doi$$a10.1016/j.neuroimage.2017.04.015
000229954 022__ $$a1053-8119
000229954 02470 $$2ISI$$a000405460900041
000229954 037__ $$aARTICLE
000229954 245__ $$aTransient networks of spatio-temporal connectivity map communication pathways in brain functional systems
000229954 260__ $$bElsevier$$c2017$$aSan Diego
000229954 269__ $$a2017
000229954 300__ $$a13
000229954 336__ $$aJournal Articles
000229954 520__ $$aThe study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural oscillations and anatomical-connectivity maps, as derived from functional and diffusion MRI, in a multilayer-graph framework that captures transient networks of spatio-temporal connectivity. These networks group anatomically wired and temporary synchronized brain regions and encode the propagation of functional activity on the structural connectome. In a group of 71 healthy subjects, we find that these transient networks demonstrate power-law spatial and temporal size, globally organize into well-known functional systems and describe wave-like trajectories of activation across anatomically connected regions. Within the transient networks, activity propagates through polysynaptic paths that include selective ensembles of structural connections and differ from the structural shortest paths. In the light of the communication-through-coherence principle, the identified spatio-temporal networks could encode communication channels' selection and neural assemblies, which deserves further attention. This work contributes to the understanding of brain structure-function relationships by considering the time-varying nature of resting-state interactions on the axonal scaffold, and it offers a convenient framework to study large-scale communication mechanisms and functional dynamics.
000229954 6531_ $$aResting-state fMRI
000229954 6531_ $$aDiffusion MRI
000229954 6531_ $$aBrain connectivity
000229954 6531_ $$aMultilayer networks
000229954 6531_ $$aTemporal networks
000229954 6531_ $$aBrain dynamics
000229954 6531_ $$aPoint-process
000229954 6531_ $$aCommunication-through-coherence
000229954 6531_ $$aSpatio-temporal connectome
000229954 6531_ $$aLTS5
000229954 700__ $$0245298$$g175081$$aGriffa, Alessandra
000229954 700__ $$0246772$$g229699$$aRicaud, Benjamin
000229954 700__ $$0245769$$g204172$$aBenzi, Kirell
000229954 700__ $$0241065$$g140163$$aBresson, Xavier
000229954 700__ $$0242941$$g193105$$aDaducci, Alessandro
000229954 700__ $$aVandergheynst, Pierre$$g120906$$0240428
000229954 700__ $$aThiran, Jean-Philippe$$g115534$$0240323
000229954 700__ $$aHagmann, Patric
000229954 773__ $$j155$$tNeuroimage$$q490-502
000229954 909C0 $$xU10954$$0252394$$pLTS5
000229954 909C0 $$xU10380$$0252392$$pLTS2
000229954 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:229954
000229954 917Z8 $$x175081
000229954 917Z8 $$x148230
000229954 937__ $$aEPFL-ARTICLE-229954
000229954 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000229954 980__ $$aARTICLE